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last modified 2009-02-02 20:14

Study of Russian Air Pollution Sources and their Impact on

Atmospheric Composition in the Arctic, using the TROICA railway carriage, data from Svalbard, and the FLEXPART transport model 

A joint project by the

Norwegian Institute for Air Research, Kjeller, Norway, and the

Obukhov Institute of Atmospheric Physics, Moscow, Russia,

proposed in the framework of the Norwegian Cooperation Programme on Research and Higher Education with Russia 2007-2010 

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1. Project summary

RAPSIFACT will interpret existing and conduct new measurements of greenhouse gases, gaseous air pollutants, and aerosols in Russia, which is the most important source region for Arctic air pollution. This has especially high relevance and is extremely urgent because climate change is proceeding the fastest in the Arctic, and reducing the emissions of short-lived pollutants in the source regions affecting the Arctic is probably the only feasible short-term strategy for slowing Arctic climate change (Quinn et al., 2007b). The proposal builds on the combination of a unique Russian measurement platform - the instrumented TROICA railway carriage -, data from several Russian air chemistry measurement stations, the FLEXPART Lagrangian tracer transport model, as well as measurements obtained at the Norwegian Zeppelin research station in Svalbard and other sites in northern Scandinavia. Three new TROICA missions will be conducted within the project, two of which will use the railroad from Kislovodsk to Murmansk, the country’s only all-weather northern port and an Arctic pollution hot spot; the third trip will be along the Trans-Siberian railroad. The focus of the study using data from ten previous and three new missions will be on the measurements taken in air masses travelling across the Barents Sea into the Arctic. The data will be compared to measurements from Svalbard to assess the impact of Russian sources onto the Arctic. A sectoral focus will be on the oil and gas industry whose emissions in the Arctic are expected to grow considerably in the future.


The objectives of RAPSIFACT are to

  • Strengthen relationships between Russian and Norwegian researchers in the field of the atmospheric and environmental sciences.
  • Quantify emissions of greenhouse gases (especially methane) and air pollutants (especially black carbon and tropospheric ozone precursors) from the oil and gas industry, e.g., leakages from pipelines and freight trains, emissions from refineries and oil production sites.
  • Compare the emissions from the oil industry with other emissions in Russia, including those from agricultural fires in western Russia and large-scale boreal forest fires in Siberia.
  • Explore the frequency distributions of concentrations of greenhouse gases, gaseous air pollutants and aerosols in the atmosphere above Russia, especially in air masses moving across the Barents Sea and into the Arctic.
  • Investigate the impact of the Russian emissions on the atmospheric composition observed in Svalbard. In particular, quantify the Russian contributions to the black carbon and ozone load.
  • Explore Russian options for reducing black carbon and ozone concentrations in the Arctic.

2. Background

2.1 Trace gas and aerosol sources in Russia and their effects in the Arctic

Effects of air pollution in the Arctic. Even though early Arctic explorers had already noticed atmospheric haze and dirty deposits on the snow (Nordenskiöld, 1883; Garrett and Verzella, 2007), the remote Arctic atmosphere was long believed to be extremely clean. However, in the 1950s, pilots flying over the North American Arctic observed widespread haze (Mitchell, 1957) that could be seen every winter and early spring. It took until the 1970s for scientists to realize that the haze was air pollution transported from the middle latitudes (Barrie, 1986). The haze is due to aerosol particles, consisting of sulphate and particulate organic matter, with smaller amounts of ammonium, nitrate and black carbon (BC) (Quinn et al., 2007a), and is accompanied also by high levels of various gaseous pollutants (e.g., non-methane hydrocarbons) and toxic compounds (e.g., persistent organic pollutants). Deposition of haze particles leads to acidification problems, and some toxic compounds can accumulate in the Arctic food chain.

Recently, awareness has grown that air pollution also affects Arctic climate. Climate change is proceeding fastest in the Arctic, and while most of the predicted future change is due to long-lived greenhouse gases and feedback processes in the Arctic, it is likely that air pollution also has a warming effect on the Arctic (see review by Law and Stohl, 2007). Several processes all go into the same direction: BC aerosols are most effective in the Arctic in absorbing solar radiation because of the high reflectivity of the underlying snow and ice surface; deposition of BC on snow/ice reduces the surface albedo, potentially a particularly powerful effect leading to enhanced snow/ice melting (Hansen and Nazarenko, 2004); a recently discovered indirect effect of aerosols produces increased longwave emissivity of thin Arctic clouds, leading to surface warming (Garrett and Zhou, 2006); and ozone causes a larger radiative forcing in the Arctic than anywhere else (Shindell et al., 2006). These effects were recently discussed in a workshop in New York as reported by Quinn et al. (2007b). While the magnitude of these effects is highly uncertain, their combined impact on Arctic surface temperatures and snow/ice melting could be of a comparable magnitude as that of the current long-lived greenhouse gas forcing. Thus, reducing the emissions of BC and ozone precursors in the source regions influencing the Arctic could have an immediate beneficial effect on the Arctic climate. It might be the best (and perhaps the only feasible) short-term strategy to slow down climate change in the Arctic. On the other hand, reductions in greenhouse gas emissions, while essential in the long term, would have no immediate effect on the Arctic climate because of their long lifetime.

The reductions in short-lived pollutants would be most effective if they were taken as soon as possible, before greenhouse gases completely dominate Arctic climate change. However, the poor understanding of the impact of short-lived pollutants on Arctic climate creates pressing research needs. In a scientific workshop following up the New York meeting, which will be organized by the co-ordinator of this proposal (see, and in a related policy workshop, researchers and environmental policy experts from many countries will convene in Kjeller from 5-8 November 2007 to discuss these issues and work on finding a consensus for an optimum abatement strategy. Further workshops are also planned. RAPSIFACT will provide crucial input to this process, as it will study pollutants directly in the source region that is most influential for the Arctic and during the most relevant times of the year. It will also build bridges to Russian scientists who can advise the Russian government when it comes to planning actual emission reductions.

Russian air pollution sources. Relatively little information is available on the sources of greenhouse gases and air pollutants in the vast continental area covered by Russia. Trace gases and aerosols are emitted in Russia from anthropogenic (e.g., traffic and industry), natural (e.g., wetland emissions of methane), and mixed (e.g., biomass burning) sources. However, a reliable and accurate emission inventory is not available for any of these sources, and global inventories (e.g., the EDGAR inventory) are poorly constrained by observations in this area.

One large source of air pollution and greenhouse gases is the oil and gas industry. The annual pollutant and greenhouse gas emissions from “Gasprom” (Russian gas monopoly) activities alone totalled 2.35 million tons in 2005 (Fig. 1), of which methane was the dominant compound. Gasprom’s annual emissions also show a growing trend due to their expanding actitivites (Gasprom Ecological Report, 2005). The emissions of nitrogen oxides (important precursors of tropospheric ozone) are growing particularly fast (by almost 6% from 2004 to 2005), due to the enhanced natural gas production. No information on BC emissions is available but they are likely to grow considerably, too.

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Figure 1. Pollutant and greenhouse gas emissions (total 2.35 million tons) by the Russian gas monopoly Gasprom (taken from Gasprom Ecological Report, 2005).

Methane emissions in Russia are dominated by fluxes from swamps in summer (Zhuang et al., 2004) and emissions from the gas industry. It is reported that methane emissions from the gas industry are stable at about 1.5 million tons per year (Gasprom Ecological Report, 2005). However, important sources such as pipeline leakages are not properly assessed and missing from these statistics. TROICA campaigns have revealed considerable influence of emissions from the Western Siberian gas fields and industries on the atmospheric composition (Tarasova et al., 2007) and numerous cases of gas pipeline leakages have been found - an example is shown in Fig. 2. Although Gasprom invested substantially in its transportation system, gas leakages are still important. Oil and gas products are also transported by freight trains, and the volume of this freight traffic is growing fast (Russian Railway official website TROICA observations have recorded many cases with increased concentrations of many pollutants (e.g., of non-methane hydrocarbons, a family of tropospheric ozone precursors) when the railway laboratory passed freight trains.

A particularly large source of air pollutants in Russia is biomass burning. Boreal forest fires in Siberia can emit huge amounts of BC and ozone precursors (Lavoué et al., 2000). Stohl (2006) has suggested that they are the single largest source of BC for the Arctic in the summer (when Arctic BC concentrations are relatively low, though), at least in years of strong burning. In western Russia, agricultural fires are set every spring and fall (Korontzi et al., 2006). In some years, they lead to ex-

Your browser may not support display of this image.Figure 2: Example of a gas leakage detected when the TROICA platform crossed a pipeline. Note the spike in the methane concentrations. 

tremely poor air quality in large regions and for many weeks. In fall of 2002, for instance, the air quality in Moscow was the worst in many years because of agricultural fires burning around the city (Elansky et al., 2007). Plumes from these fires can be transported over hundreds to thousands of kilometers (Niemi et al., 2004). In spring 2006, new air pollution records (of ozone, carbon monoxide, BC, aerosol optical depth, etc.) were set at the Zeppelin station when such a fire plume was transported across Svalbard (Stohl et al., 2007). Photographs from before and during the episode show a dramatic decrease in visibility when the plume engulfed Svalbard (Fig. 3). Deposition of BC aerosols even led to a documented reduction of the surface albedo on a glacier in Svalbard during this episode (Stohl et al., 2007), and fire emissions also contributed substantially to other strong pollution events at Zeppelin (study in progress).

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Figure 3: Photographs from the Zeppelin research station taken before (left) and during (right) the arrival of a smoke plume from agricultural fires in Eastern Europe (mainly Russia). Image courtesy of Ann-Christin Engvall.

Atmospheric composition monitoring data in Russia. Air quality monitoring data are available mainly from major cities, e.g., Moscow (Elansky et al., 2007), which are not representative of the larger-scale “background”, and which are therefore difficult to interpret. Neither in the European part of the country, nor in Siberia east of the Ural mountain range, exists a monitoring network, from which reliable information on the atmospheric background concentration levels of greenhouse gases, aerosols and gaseous air pollutants could be obtained. The need for more high-quality atmospheric “background” composition data has produced several initiatives to establish platforms for providing this information. We will utilize the TROICA (Trans-Continental Observations into the Chemistry of the Atmosphere) instrumented railway carriage, a proven platform created a decade ago (Crutzen et al., 1998) that will be described below. Other initiatives are the U.S.-Russian collaboration to establish, within the next few years, an air chemistry laboratory in Tiksi at the mouth of the Lena River, or the French-Russian YAK-AEROSIB initiative to perform research flights over Russian territory. Russian scientists have also established a few stations with continuous monitoring of key compounds, and we will have access to data from three stations.

Relevance of Russian sources for air pollution levels in the Arctic. In most parts of the world, there are virtually no pollution sources north of 60°N. However, in Russia large sources can be found even north of the Arctic Circle, like the metal smelters in Norilsk and Nikel (AMAP, 2006), or Murmansk, the country’s only all-weather northern port in the Barents Sea (Eglington et al., 1998). Even biomass burning occurs at high latitudes in Russia. Furthermore, high-latitude oil and gas drilling activities are expected to grow substantially during the next decades, and plans also exist for substantial offshore drilling in the Barents Sea. Furthermore, if the Arctic sea ice will disappear as a result of Arctic warming (Holland et al., 2006), ships will use the Northeast Passage more frequently, from which dramatic increases in Arctic ozone (Granier et al., 2006) and other pollutants are expected.

The prevailing meteorological conditions further enhance the relevance of Russian pollution sources, particulary during the Arctic haze season. Surfaces of constant potential temperature form closed domes over the Arctic, with minimum values in the Arctic boundary layer (Klonecki et al., 2003). This isolates the Arctic lower troposphere from the rest of the atmosphere by a transport barrier, the so-called Arctic front. Meteorologists realized that in order to facilitate isentropic transport, a pollution source region must have the same low potential temperatures as the Arctic haze layers (Iversen, 1984, Barrie, 1986). In Eurasia, the Arctic front can be located as far south as 40ºN on average in January (see Fig. 1 in Barrie, 1986), thus, connecting the atmosphere above Russia directly to the Arctic. Furthermore, Russia is on a preferred pathway into the polar dome that involves diabatic cooling of air travelling over snow-covered land (Barrie, 1986, Stohl, 2006). In contrast, air masses leaving North America's or Southeast Asia’s densely populated east coasts are heated diabatically over the downwind oceans and cannot reach the Arctic so easily (Stohl, 2006).

In summary, Russian pollution sources are located far north, and they are also located along the main entrance pathway of air masses into the Arctic. Both facts make Russian air pollution sources much more relevant for the air pollution levels in the Arctic than sources in other regions.


Our proposed work builds on the TROICA measurement platform. It consists of a laboratory railway carriage that can be attached to normal passenger trains and was specifically outfitted for the measurement of atmospheric trace gases and aerosols by the Obukhov Institute of Atmospheric Physics (OIAP). The first TROICA expedition was performed in a collaborative study with the Max-Planck-Institute for Chemistry (Mainz, Germany) in 1995 and, since then, ten expeditions were performed by OIAP, some of them also in collaboration with other institutes (e.g., Helsinki University). Recently, two special railroad cars were specifically built for TROICA, one of which is used for in-situ atmospheric observations, while the other is a chemical laboratory for analyzing air, precipitation, soil and other samples (see title page and Fig. 4). Most of the expeditions travelled along the Trans-Siberian railroad between Moscow and Vladivostok, a two-way trip of almost 20.000 km but some campaigns took other routes. For instance, the most recent expedition in 2006 circled Moscow several times to study the impact of the emissions from this megacity on regional air quality. The railway carriage is equipped with instruments measuring O3, SO2, NO, NO2, NOx, NH3, CO, CO2, CH4, non-methane hydrocarbons, 222Rn, aerosol light absorption (equivalent BC), aerosol size distributions and particle concentrations in several size ranges, and meteorological parameters. A complete list of the instruments used, their measurement ranges, time resolution, etc., is attached to this application in a separate document.

The results of past TROICA expeditions have been documented extensively in the scientific literature (e.g., Crutzen et al., 1998; Oberlander et al., 2002; Belikov et al., 2006; Tarasova et al., 2006, 2007; Vartiainen et al., 2007; Kuokka et al., 2007). One new TROICA trip has been funded already by the Norwegian Research Council (but the activity budget was cut by 70%) in the framework of the International Polar Year project POLARCAT and will be performed in the summer 2008 as part of a large international campaign. 

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Figure 4: Inside view of the TROICA carriage (left) and the Russian railroad network (right).

2.3 Zeppelin station and other measurement sites

NILU is operating the research station Zeppelin on Svalbard (11.9°E, 78.9° N, 478 m asl, see The station is situated in an unperturbed Arctic environment on a ridge of Zeppelin mountain on the western coast of Svalbard. Because of the altitude difference and the generally stable atmospheric stratification, contamination from the nearby small settlement of Ny Ålesund (located near sea level) is minimal at Zeppelin. The station is one of the most well equipped Arctic laboratories, and it is ideally located in the pathway of air pollution travelling from western Russia across the Barents Sea towards the high Arctic. A large range of greenhouse gases, acidifying compounds, carbon monoxide, ozone, heavy metals, aerosol size distribution and composition, BC, etc., are measured at the station. As in past studies, we plan to collaborate in our research with Johan Ström from Stockholm University, who runs an extensive aerosol measurement program at the station.

NILU is also hosting the Chemical Coordination Centre for EMEP, and data from a large number of EMEP stations are quality assured and stored at the institute. We plan on using data from high latitude stations, which are influenced by pollution plumes from Russia, for evaluating the broader-scale impact of Russian air pollution. OIAP has access to data from the Russian stations Lovozero (68ºN, 35ºE), Kislovodsk (44ºN, 43ºE), and the 302 m high tower at Zotino (61ºN, 89ºE).


FLEXPART (see http:/ is a Lagrangian particle dispersion model that has been developed by Stohl et al. (1998, 2005) and is now used at about 40 institutes from 17 countries. The model simulates the transport of passive tracers by calculating the trajectories of a multitude of so-called particles using the resolved winds from global meteorological analyses (ECMWF or GFS) and parameterizations for turbulence and convection. It does not include any chemical processes but a highly accurate transport model that has been used, for instance, as a forecast tool for the flight planning in field campaigns using research aircraft, the interpretation of data from surface stations, aircraft, or remote sensing platforms, and for producing transport climatologies. A speciality of the model is the possibility to run it backward in time to compile information on the spatial distribution of sources contributing to measured air pollution events (Stohl et al., 2003). Figure 5 shows as an example a so-called emission sensitivity map for the previously mentioned recent extreme pollution event at the Zeppelin station (Stohl et al., 2007). It shows where sources could have potentially contributed to the measured air pollution over the course of the last 20 days of transport. It also shows where agricultural fires were detected (black dots) on days and over locations with high emission sensitivity. These, other FLEXPART model products not shown here, and the measurements allowed the unambiguous attribution of the measured record-high pollution levels to the agricultural fires.

NILU has also developed tools to display the rather comprehensive information produced by FLEXPART via internet webpages, where the user can navigate easily through a wealth of information. Examples for the available products are a webpage for the Zeppelin research station at Svalbard ( or a wegpage for a recent international aircraft campaign ( Both websites contain hundreds of thousands of plots and navigation tools for efficient browsing.

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Figure 5. Footprint potential emission sensitivity for an air mass arriving at Zeppelin station on 2 May 2006 between 21 and 24 UTC. This air mass led to the highest pollution levels ever observed at the station, and also produced a visible reduction of the snow albedo. Red dots indicate satellite-detected fires on forested land, black dots are fires on agricultual land. 

3. Proposed work

Three new TROICA missions. We will perform three new TROICA missions. Two will travel along a meridional transect using the railroad from Kislovodsk to Murmansk, one will travel in zonal direction from Moscow to Vladivostok. The Kislovodsk-Murmansk trips are tentatively planned for March 2009 and April 2010, the Moscow-Vladivostok expedition for July 2009. An alternative route for one of the three trips might also be the railroad to Vorcuta, located at nearly the same latitude as Murmansk but further east (see Fig. 4). For our purposes, we want to avoid the winter period when even the day-time atmospheric boundary layer is not well mixed. Such conditions would make the inversion of the model results more uncertain and, thus, the emission source strength determination more difficult. The two spring expeditions are planned for a time of the year when normally many agricultural fires are burning in western Russia and when it is also probable that these emissions are transported towards the Barents Sea. We suspect that these biomass burning emissions represent the strongest source of ozone precursors and BC at this time of the year which is especially critical for snow and ice melt at high latitudes. At the same time, this pollution source could potentially be eliminated completely through banning of these fires (as in many other countries). Thus, finding convincing evidence that these fires have a strong detrimental influence on air quality and the Arctic climate is a major objective of the two spring missions. The summer mission would probably encounter biomass burning plumes from boreal forest fires. All three missions, thus, would likely allow comparing the atmospheric concentrations in biomass burning plumes with such in pollution plumes from Russian cities and industries. Detailed case studies of pollution plumes encountered during the three new missions will be made, using also tools described below and used for a statistical analysis of the entire TROICA data set. The focus would be on cases where the air masses are subsequently transported into the Arctic. For such cases we would also use measurement data from Zeppelin and the other stations. Ideally, the TROICA mission would observe the pollution close to the Russian sources, and other stations in Northern Scandinavia or Svalbard would observe the same polluted air mass hundreds or thousands of kilometres further north. Of course, the possibility of such a strict Lagrangian study will depend on the meteorological conditions.

Analysis of pollution plume encounters. FLEXPART will be run backward and forward from the tracks of all ten previous TROICA missions and the three new ones planned as part of this project, as well as from the three Russian stations. The backward simulations will be used to characterize the sources of the observed air pollution, whereas the forward simulations will be used to establish two data subsets according to whether the observed air mass would later be transported towards the Arctic, or not. Statistical comparisons of the average and extreme concentrations for the two subsets (which will be further sub-divided according to the geographical location where measurements were taken) will be made for all measured chemical species. Differences in the sources contributing to the measured pollution will be identified for the various subsets.

For species that can be considered passive tracers over long enough (order of 10 days) time scales (carbon monoxide, methane), we will compare FLEXPART results directly with the measurements to learn about possible biases in the EDGAR (or an equivalent better resolved) emission inventory underlying the simulations (Olivier and Berdowski, 2001). This will allow correcting the emission inventories for subsequent simulations. We will then use ratios of the enhancements of NOx and BC with respect to the carbon monoxide enhancement (derived from regression analyses) to learn about the typical pollution mix close to urban and industrial sources, which can be compared to the emission ratio derived from the emission inventories. Using this method, we will be able to improve the emission inventories also for these important shorter-lived species.

Sections along which biomass burning plumes were encountered will also be identified, using both the observations and the FLEXPART model products that will use an emission model based on fire detections from MODIS and/or ATSR, landuse information, and possibly areas burned (when available). We will compare the pollutant and greenhouse gas concentrations in the biomass burning plumes with those encountered outside the plumes and in anthropogenic pollution plumes. We will also calibrate our emission algorithm with data obtained relatively close to the fires, to estimate total biomass burning emissions from agricultural and boreal forest fires.

Emissions from the oil and gas industry. In Fig. 2 we have shown a case where the railroad was crossing a leaking gas pipeline and where enhanced concentrations of methane and non-methane hydrocarbons were observed. Over large stretches, the railroad also runs parallel to pipelines and, thus, numerous such cases are documented in the TROICA record and can be identified by their unique chemical signature. However, these events have never been analyzed completely. For every significant event, we will run FLEXPART driven with global ECMWF analysis data and downscaled with the meteorological data observed on the train, when available and reliable (see Stohl et al., 1997 for a description of the downscaling method). Inversion of the model results will then allow determining the methane source strength. Very simple inversion methods can be used, as the location of the point source is known. While mesoscale model simulations would yield a better basis for driving FLEXPART, we anticipate doing this for a large number of events observed during a period of more than ten years, for which this is impractical. We will proceed by testing whether we can find trends in the frequency or severeness of such events over the period of 14 years in total. An extrapolation of the average derived source strengths to the total length of the gas pipeline network in Russia will yield a rough estimate of methane and non-methane hydrocarbon emissions from pipeline leakages.

A similar approach will be taken for cases when the train passed close by a known petrochemical installation or when it met a freight train transporting oil products (information on the opposing traffic is partially available). For the former, emissions of species such as NOx and BC will also be considered. Altogether, these studies will yield unique information about the emissions from the petrochemical industry, which can be compared to the emissions from other sources.

Impact of Russian air pollution plumes on the Arctic. We will use 20 years of 3-hourly FLEXPART backward calculations (which will be available from the POLARCAT project) for the Zeppelin station to calculate Russian contributions to the simulated total carbon monoxide. Using an appropriate threshold (e.g., more than 10 ppb of carbon monoxide from Russia transported within the last 20 days), we will identify time periods with substantial influence from Russian sources, western European sources, mixed Russian and other sources, fire-influenced episodes, and clean periods. For these time periods, we will investigate the chemistry and aerosol measurements available from the station to compare the impact of Russian and other sources on the chemical composition over Svalbard. For species with long records (e.g., sulphate, ozone are available since 1989), we will also investigate trends in air masses transported from the different source regions.

We will compare the average measured enhancement ratios between short-lived pollutants (e.g., sulphate, BC) and the relatively longer-lived tracer carbon monoxide in Russian-influenced air masses to those measured by TROICA directly in Russia. The difference is a measure of the removal of the shorter-lived pollutants en route, which is a critical factor in determining the Arctic pollution load. Together with forward tracer model results like those described in Stohl (2006), seasonally varying removal factors can also be used to roughly estimate the pollutant input from Russian sources into the Arctic. Using scenarios for the future development of the emissions in the high latitudes of Russia, we can then also estimate future changes in concentration levels.

Strategies to reduce BC and ozone precursor concentrations in the Arctic. As an outcome of this project, we will have a better understanding of the sources of BC and ozone precursors in Russia. We will be able to more accurately constrain the emissions from agricultural fires, boreal forest fires, the oil and gas industry, and other sources. We will also have a better understanding of the impact of these sources on the BC and ozone concentrations in the Arctic, as well as of the relative roles of high- and lower-latitude sources in Russia. Consequently, we could inform Russian policy makers to optimize their emission reduction strategies in a way that yields the greatest benefit for the Arctic. In the light of the current discussion on the climatic effects of short-lived pollutants in the Arctic, this will be an important result of RAPSIFACT, which can be used also in the international arena.

4. Coordination, partner roles, international collaboration, relation with other projects

NILU will be responsible for the overall coordination of the project, communication with the Norwegian Research Council, and outreach activities. NILU will perform all FLEXPART transport model calculations. OIAP will be responsible for carrying out the TROICA missions; however, both NILU and OIAP scientists will discuss their overall strategy, routes and timing. NILU and OIAP scientists will perform data analyses together, whereby OIAP scientists would concentrate on aspects where local knowledge is essential (e.g., pipeline leakages, local pollution sources), whereas NILU would concentrate on the transport of pollution into the Arctic.

We intend to collaborate with researchers in Sweden (Johan Ström), Finland (M. Kulmala). RAPSIFACT would be closely coordinated with the International Polar Year project POLARCAT. It would benefit from the experience gained during the POLARCAT TROICA mission in 2008, and it would use the POLARCAT web portal for outreach actitivies (general information about the campaigns, short presentations about the key findings). We would probably publish the first RAPSIFACT results in a special issue organized by POLARCAT. RAPSIFACT would also contribute to the planned assessments of the effects of short-lived pollutants on the Arctic climate.

6. References

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Belikov, I. B., C. A. M. Brenninkmeijer, N. F. Elansky, and A. A. Ral’ko (2006): Methane, carbon monoxide, and carbon dioxide concentrations measured in the atmospheric surface layer over continental Russia in the TROICA experiments. Izvest. Atmos. Ocean. Phys. 42, 46-59.

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Korontzi, S., J. McCarty, T. Loboda, S. Kumar, and C. Justice, C. (2006): Global distribution of agricultural fires in croplands from 3 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data, Glob. Biogeochem. Cyc. 20, GB2021, doi:10.1029/2005GB002529.

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